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Learning Path

Production AI Reliability

Make AI systems observable, recoverable, secure and releaseable.

Learning goal: Diagnose failures from traces and make evidence-backed release decisions.
What you will build: A SHIP / BLOCK / INCONCLUSIVE decision with explicit vetoes and fallback.

Local learning evidence

0 / 19 available Guides read0 / 2 Practices completed

Counts only published Guides and Practice completion signals; page views are never presented as mastery.

Continue learning: Timeout ambiguity

Course structure

01

Recover from ambiguous failures

4 topics

02

Evaluate and observe

9 topics

03

Enforce trust and human boundaries

4 topics

04

Architect and release

5 topics

Cross-layer architecture decompositionConcept · Guide expanding
Rollout and fallbackBounded rollout, fallback and graceful degradationGuide
7 min
SHIP / BLOCK / INCONCLUSIVEEvidence synthesis into SHIP / BLOCK / INCONCLUSIVEGuide
7 min
Queues, concurrency and backpressureConcept · Guide expanding
Versioned dependencies and change boundariesVersion external capabilities explicitlyGuide
6 min